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BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Kelvin C. K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy

arXiv:2012.02181v2cs.CV

TL;DR

VSR requires complex handling of temporally related but misaligned frames, motivating a search for generic and efficient baselines. The paper decomposes VSR into four functionalities and proposes BasicVSR using minimally redesigned components, then extends it to IconVSR. BasicVSR improves performance by up to 0.61 dB and efficiency by up to 24×, while IconVSR surpasses BasicVSR by up to 0.31 dB PSNR.

  • Problem

    VSR must aggregate information from multiple highly related but misaligned frames, while existing designs can be complex and difficult to implement, extend, reproduce, and compare.

  • Method

    The paper decomposes VSR into propagation, alignment, aggregation, and upsampling, analyzes component options, and builds BasicVSR with minimal redesigns before adding information-refill and coupled propagation in IconVSR.

  • Results

    BasicVSR outperforms existing state-of-the-art methods by up to 0.61 dB with up to 24× speedup, while IconVSR improves PSNR over BasicVSR by up to 0.31 dB.

  • Takeaways & Limitations

    BasicVSR and IconVSR provide strong, simple, efficient, and extensible baselines for future VSR research and potentially other low-level vision tasks.

  • Takeaways & Limitations

    BasicVSR produces imbalanced information reception across frames, so earlier frames are expected to have suboptimal results.

Abstract

from arXiv · show

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

1. Introduction

VSR must aggregate information from multiple misaligned frames, but existing designs often trade efficiency, simplicity, and reproducibility for performance. BasicVSR reconsiders four core functionalities and shows that minimal redesigns can produce an efficient, extensible baseline with strong restoration quality.

  • VSR aggregates information from multiple highly related but misaligned frames, creating challenges beyond single-image super-resolution.
  • Complex alignment and aggregation designs can increase runtime and model complexity while making implementation, extension, reproducibility, and fair comparison more difficult.
  • Existing VSR approaches can be decomposed into propagation, alignment, aggregation, and upsampling functionalities for systematic component analysis.
  • BasicVSR uses bidirectional propagation, optical-flow alignment, feature concatenation, and pixel-shuffle upsampling to form a strong efficient baseline.
  • 0.61 dB and 24×: BasicVSR improves performance and efficiency over existing state-of-the-art methods, respectively.
  • IconVSR extends BasicVSR with information-refill and coupled propagation, surpassing BasicVSR by up to 0.31 dB PSNR.
  • The paper argues that properly integrated simple components can synergize into state-of-the-art performance and provide an extensible VSR baseline.

2. Related Work

Related VSR work uses sliding-window and recurrent frameworks, with alignment ranging from optical-flow warping to implicit deformable methods. BasicVSR instead emphasizes generic propagation branches and connects interval-based processing to enable long-term information flow.

  • Existing VSR methods are mainly organized into sliding-window and recurrent frameworks.
  • Sliding-window methods commonly estimate optical flow between low-resolution frames and spatially warp them for alignment.
  • Later approaches use implicit feature alignment, including deformable convolutions and multi-scale deformable alignment.
  • BasicVSR uses a bidirectional recurrent network whose propagation branches contain generic components, including flow estimation, spatial warping, and residual blocks.
  • Connecting interval-based processing through propagation branches allows long-term information to pass across intervals and improves aggregation effectiveness.

3. Methodology

BasicVSR reduces VSR to propagation, alignment, aggregation, and upsampling choices, selecting bidirectional long-term propagation and feature-level flow alignment. IconVSR extends this baseline with information refill and coupled propagation to refine features and exchange information.

  • BasicVSR design: BasicVSR decomposes VSR into propagation, alignment, aggregation, and upsampling, providing a simple framework for analyzing component choices.The method is designed as an efficient and versatile baseline built from commonly adopted elements.
  • Propagation: Reducing the number of temporal segments increases the receptive field and improves PSNR, showing that distant frames and long-term information benefit restoration.The largest PSNR differences occur at segment ends, emphasizing the value of long sequences for accumulating information.
  • Propagation: Bidirectional propagation addresses the information imbalance of unidirectional propagation, whose early frames receive less information and show lower PSNR.With unidirectional propagation, a consistent 0.5 dB performance drop is observed when only partial information is used.
  • Alignment: Feature-level flow alignment is preferred because removing alignment drops PSNR by 1.19 dB, while image alignment drops PSNR by 0.17 dB.Feature alignment avoids the blur and incorrectness introduced when inaccurately estimated flow warps images before restoration.
  • BasicVSR design: BasicVSR combines bidirectional global propagation, feature-level flow alignment, feature concatenation, and pixel-shuffle upsampling into a succinct, efficient pipeline.The resulting baseline achieves strong restoration quality and efficiency and can accommodate additional components.
  • IconVSR extension: IconVSR adds information refill and coupled propagation to mitigate propagation errors and improve information aggregation without computational overhead from branch reconnection.Information refill extracts keyframe features for refinement, while coupled propagation passes backward-branch outputs to forward branches.

4. Experiments

The experiments evaluate BasicVSR and IconVSR across multiple datasets, degradations, and competing models, showing strong restoration quality and efficiency. BasicVSR improves over several state-of-the-art methods, while IconVSR provides further gains with slightly longer runtime.

  • Datasets and Settings: BasicVSR and IconVSR are evaluated on REDS, Vimeo-90K, Vid4, and UDM10 using 4× downsampling under Bicubic and Blur Downsampling degradations.REDS4 is used for testing, REDSval4 for validation, and Vimeo-90K-T is included as an additional test set.
  • Datasets and Settings: The comparison covers 14 existing VSR models, with parameters counted inclusively for BasicVSR and IconVSR's SPyNet flow network.This setup is intended to make the model-size comparison fair.
  • BasicVSR: 0.61 dB: BasicVSR surpasses RSDN on UDM10 while using a similar number of parameters.Against EDVR, BasicVSR achieves a 0.33 dB improvement on REDS4 despite EDVR's significantly larger complexity.
  • BasicVSR: BasicVSR slightly underperforms sliding-window methods on Vimeo-90K-T, consistent with that dataset's seven-frame sequences and BasicVSR's reliance on long-term information aggregation.The paper reports competitive performances on Vimeo-90K-T and Vid4 compared with EDVR.
  • IconVSR: Up to 0.31 dB: IconVSR improves over BasicVSR with slightly longer runtime, especially on Vimeo-90K-T and REDS4.Qualitatively, both methods recover finer details and sharper edges, while IconVSR reconstructs sharper edges with its proposed components.

5. Ablation Studies

The ablations examine information-refill and coupled propagation as extensions to BasicVSR, showing how they recover details lost through imperfect alignment and propagation. Keyframe count also trades inference speed against PSNR in IconVSR.

  • Qualitative Comparisons: BasicVSR and IconVSR recover clearer square patterns on REDS4, while IconVSR additionally restores sharper edges.The comparison is qualitative and should be viewed after zooming in.
  • Qualitative Comparisons: BasicVSR and IconVSR alone recover vertical strip patterns on Vimeo-90K-T, with IconVSR producing sharper edges.The figure reports a qualitative comparison across methods.
  • Information-Refill: Information-refill restores features lost where spatial warping has non-existing correspondences, enabling subsequent refinement and propagation.The mechanism uses additional features to refill poorly aligned regions.
  • Information-Refill: Information-refill improves fine-detail restoration where neighboring-frame aggregation is impaired by alignment errors, including clearer license-plate numbers.Its contribution is especially apparent in regions with fine details.
  • Coupled Propagation: Coupled propagation uses backward-propagated features more effectively, reconstructing more details and finer edges in occluded or persistent regions.The ablation disables information-refill to isolate the coupled-propagation effect against BasicVSR.
  • Keyframe Tradeoff: 30.38 dB PSNR is achieved by IconVSR with no keyframes on REDSval4, 0.21 dB higher than BasicVSR.With no keyframes, IconVSR degenerates to a recurrent network; PSNR increases with the number of keyframes.

6. Conclusion

The paper presents BasicVSR as a simple, efficient, and effective VSR baseline, then extends it with IconVSR for further performance improvements. Both models are positioned as baselines for future VSR work.

  • Conclusion: BasicVSR is a simple yet effective network that outperforms existing state-of-the-art methods with high efficiency.The paper frames generic and efficient baselines as a way to ease VSR analysis and extension.
  • Conclusion: IconVSR builds on BasicVSR with two novel components to further improve performance.The conclusion identifies BasicVSR and IconVSR as strong baselines for future work.
  • Conclusion: The architecture design findings could potentially extend to video deblurring, denoising, and colorization.This is presented as a potential extension to other low-level vision tasks.

A. Architecture and Experimental Settings

The experiments use shared flow-estimation and propagation-network settings across BasicVSR and IconVSR, with keyframes and an additional feature extractor used for IconVSR. Training and evaluation cover REDS, Vimeo-90K, and several established test sets under 4× downsampling.

  • Architecture: BasicVSR and IconVSR use SPyNet for flow estimation, 30 residual blocks per propagation branch, and 64 feature channels.IconVSR uses EDVR-M5 as its additional feature extractor, while both models share the flow estimator and main network.
  • Architecture: IconVSR selects every five frames as keyframes, applying its additional feature extractor only to those frames so its computational burden is insignificant.The feature extractor and main network are fine-tuned together.
  • Experimental Settings: Training uses 15-frame REDS sequences, 14-frame temporally augmented Vimeo-90K sequences, and whole video sequences during inference.The Vimeo-90K sequence is temporally flipped to allow longer propagation.
  • Experimental Settings: The optimization setup uses Adam, cosine annealing, 300K iterations, batch size 8, and 64×64 low-resolution input patches.The flow estimator and feature-extractor weights are fixed during the first 5,000 iterations.
  • Experimental Settings: The models are trained and tested with 4× downsampling using Bicubic and Blur Downsampling degradations.Blur Downsampling applies Gaussian filtering with σ=1.6 before subsampling every four pixels.

B.1. Comparison with State of the Arts

Qualitative comparisons across REDS4, Vimeo-90K, Vid4, and UDM10 show that BasicVSR and IconVSR produce finer details and sharper edges. IconVSR further improves output quality with information-refill and coupled propagation.

  • Qualitative Comparison: BasicVSR and IconVSR successfully produce outputs with finer details and sharper edges across the reported qualitative comparisons.The comparisons cover REDS4, Vimeo-90K, Vid4, and UDM10.
  • Qualitative Comparison: IconVSR reconstructs images with sharper edges after adding information-refill and coupled propagation.The paper attributes the further qualitative improvement to the proposed components.

B.2. BasicVSR vs IconVSR

IconVSR extends BasicVSR with information-refill and coupled propagation, improving fine-region quality and edge sharpness in visual comparisons. The section also reports qualitative comparisons across REDS, Vimeo-90K, Vid4, and UDM10, alongside representative restoration scores.

  • IconVSR improvements: Information-refill improves fine-region output quality where alignment is error-prone.The comparison attributes this improvement to replenishing information in difficult fine regions.
  • IconVSR improvements: Coupled propagation produces sharper edges by better employing long-term sequence information.This visual effect is reported in the BasicVSR-versus-IconVSR comparison.
  • Comparisons: 27.72 dB is reported for EDVR-M on REDS Frame 074, Clip 000, compared with 27.51 dB for RBPN.The same row reports 27.49 dB for PFNL and 24.42 dB for Bicubic.
  • Comparisons: 27.37 dB is reported for RBPN on REDS Sequence 837, Clip 001, compared with 27.34 dB for EDVR-M.The same row reports 26.52 dB for PFNL and 22.99 dB for Bicubic.
  • Comparisons: 22.17 dB is reported for EDVR-M in another comparison, compared with 22.11 dB for RBPN.The same result reports 21.74 dB for PFNL and 18.83 dB for Bicubic.
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